Language Model Security Database

Multi-Agent Systems based on Large Language Models (LLM-MAS) are vulnerable to systemic Consensus Corruption via cascading error amplification. Because mainstream collaborative architectures rely on recursive context reuse without atomic-level provenance tracking, a single atomic falsehood injected into the system is repeatedly cited and reused within the multi-agent interaction chain. This structural exposure causes the error to deterministically compound across the communication graph…

Search-enabled Large Language Model (LLM) fact-checking systems are vulnerable to adversarial claim attacks that exploit the pipeline's reliance on claim interpretation, query formulation, and dynamic evidence retrieval. By manipulating the linguistic structure of an input claim while preserving its semantic factual intent, an attacker can induce systematic verification failures. This vulnerability stems from three specific attack surfaces: 1. Search Engine Misguidance: Altering lexical…

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…

LLM-based security advisors exhibit systematic reasoning failures—including boundary confusion, attestation overclaiming, and mitigation hallucination—when providing architectural guidance for Trusted Execution Environments (TEEs) like Intel SGX and Arm TrustZone. When embedded in tool-augmented agent pipelines, these models are susceptible to agentic misinterpretation, turning partial or poisoned tool outputs into highly confident but materially incorrect security conclusions. This…

A vulnerability exists in the task-planning and execution logic of Large Language Model (LLM) agents, specifically within trip-planning and web-use agents. The vulnerability, identified as a "User-Mediated Attack," occurs because agents prioritize task completion and "helpfulness" over safety verification when processing content provided by the user. When a benign user forwards untrusted external content (e.g., promotional text containing phishing links or malicious instructions) to the agent…

A cognitive vulnerability exists in the reasoning mechanisms of autonomous Large Language Model (LLM) agents, specifically regarding "narrative overfitting"—the model's intrinsic drive to synthesize coherent causal stories from fragmented inputs. This vulnerability allows for "Cognitive Collusion Attacks" where an attacker creates a fabricated belief state in the victim agent using exclusively factually true evidence fragments. By employing a "Generative Montage" framework (consisting of…

LLM-based code agents and vulnerability detectors employing Chain-of-Thought (CoT) reasoning are susceptible to automated adversarial code obfuscation. The vulnerability exists because CoT mechanisms expose the model's decision logic, allowing reinforcement learning frameworks (such as CoTDeceptor) to iteratively refine code transformations based on the detector's own reasoning traces. By optimizing for "reasoning instability" and "hallucination" rather than just syntactic evasion, attackers…

Multi-agent Large Language Model (LLM) systems employing ensemble sampling-and-voting strategies (specifically the "Agent Forest" framework) are vulnerable to adversarial input perturbations. While increasing the number of agents ($n \in \{1, \dots, 25\}$) improves accuracy on clean inputs, the system fails to mitigate the impact of synthetic punctuation noise and human-like typographical errors. Attackers can introduce surface-level perturbations—such as random punctuation insertion (10-50%…

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

Large Language Models (LLMs) utilized for static code analysis, code review, and autonomous software engineering exhibit a cognitive vulnerability termed "Abstraction Bias." When processing code that structurally resembles common algorithmic patterns (e.g., standard sorting algorithms, helper functions, or mathematical formulas), the model relies on high-level memorized representations of the algorithm's intent rather than analyzing the specific local logic. Adversaries can exploit this by…